Overview

Dataset statistics

Number of variables14
Number of observations52560
Missing cells932
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 6 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 4 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 4 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
# Date and time has unique valuesUnique
blade_angle has 26317 (50.1%) zerosZeros
Rotor speed (RPM) has 1111 (2.1%) zerosZeros

Reproduction

Analysis started2023-07-08 11:10:48.841970
Analysis finished2023-07-08 11:11:05.837776
Duration17 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

# Date and time
Date

Distinct52560
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size410.8 KiB
Minimum2019-01-01 00:00:00
Maximum2019-12-31 23:50:00
2023-07-08T16:41:05.891141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:05.992165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct52385
Distinct (%)99.8%
Missing71
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean674.12364
Minimum-15.816108
Maximum2076.3194
Zeros0
Zeros (%)0.0%
Negative4456
Negative (%)8.5%
Memory size410.8 KiB
2023-07-08T16:41:06.126045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-15.816108
5-th percentile-1.1053409
Q1158.32399
median471.31021
Q31051.5157
95-th percentile2001.3458
Maximum2076.3194
Range2092.1355
Interquartile range (IQR)893.19168

Descriptive statistics

Standard deviation625.12329
Coefficient of variation (CV)0.92731252
Kurtosis-0.43050787
Mean674.12364
Median Absolute Deviation (MAD)378.65029
Skewness0.86572497
Sum35384076
Variance390779.13
MonotonicityNot monotonic
2023-07-08T16:41:06.221341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9314910233 3
 
< 0.1%
-1.024853528 3
 
< 0.1%
-1.042459026 3
 
< 0.1%
-2.130799055 3
 
< 0.1%
-1.540214527 2
 
< 0.1%
-1.055263022 2
 
< 0.1%
-0.925089021 2
 
< 0.1%
-0.5158945158 2
 
< 0.1%
2034.995068 2
 
< 0.1%
-0.6012545124 2
 
< 0.1%
Other values (52375) 52465
99.8%
(Missing) 71
 
0.1%
ValueCountFrequency (%)
-15.81610751 1
< 0.1%
-15.30395741 1
< 0.1%
-15.05295859 1
< 0.1%
-13.85164614 1
< 0.1%
-13.71490617 1
< 0.1%
-13.6329247 1
< 0.1%
-13.61896758 1
< 0.1%
-13.60926056 1
< 0.1%
-13.60628651 1
< 0.1%
-13.53855395 1
< 0.1%
ValueCountFrequency (%)
2076.319403 1
< 0.1%
2075.483887 1
< 0.1%
2074.425403 1
< 0.1%
2073.468756 1
< 0.1%
2073.172607 1
< 0.1%
2073.075525 1
< 0.1%
2072.071417 1
< 0.1%
2071.897467 1
< 0.1%
2071.52774 1
< 0.1%
2071.329822 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct52487
Distinct (%)> 99.9%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean203.61887
Minimum0.0072405917
Maximum359.98983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T16:41:06.317789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0072405917
5-th percentile32.067181
Q1150.8461
median219.33637
Q3267.38539
95-th percentile330.14481
Maximum359.98983
Range359.98259
Interquartile range (IQR)116.53929

Descriptive statistics

Standard deviation89.145032
Coefficient of variation (CV)0.43780339
Kurtosis-0.53275356
Mean203.61887
Median Absolute Deviation (MAD)56.591531
Skewness-0.53690865
Sum10687547
Variance7946.8366
MonotonicityNot monotonic
2023-07-08T16:41:06.415710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
277.2627869 2
 
< 0.1%
277.1826694 1
 
< 0.1%
270.5378509 1
 
< 0.1%
266.473391 1
 
< 0.1%
265.8480923 1
 
< 0.1%
268.1874589 1
 
< 0.1%
273.3554712 1
 
< 0.1%
270.9961185 1
 
< 0.1%
277.172732 1
 
< 0.1%
275.4750913 1
 
< 0.1%
Other values (52477) 52477
99.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
0.007240591718 1
< 0.1%
0.02455494917 1
< 0.1%
0.03210036848 1
< 0.1%
0.04590088129 1
< 0.1%
0.05243898567 1
< 0.1%
0.06967542867 1
< 0.1%
0.08268782613 1
< 0.1%
0.1143776964 1
< 0.1%
0.1395000834 1
< 0.1%
0.1471973775 1
< 0.1%
ValueCountFrequency (%)
359.9898306 1
< 0.1%
359.9894311 1
< 0.1%
359.9610264 1
< 0.1%
359.9074395 1
< 0.1%
359.9064448 1
< 0.1%
359.8915148 1
< 0.1%
359.8671657 1
< 0.1%
359.866624 1
< 0.1%
359.8444626 1
< 0.1%
359.84084 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct14455
Distinct (%)27.5%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean204.78981
Minimum0.062640295
Maximum359.98847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T16:41:06.516413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.062640295
5-th percentile33.168494
Q1154.99733
median219.75381
Q3269.33461
95-th percentile332.75181
Maximum359.98847
Range359.92583
Interquartile range (IQR)114.33728

Descriptive statistics

Standard deviation89.47915
Coefficient of variation (CV)0.43693166
Kurtosis-0.52292708
Mean204.78981
Median Absolute Deviation (MAD)56.561999
Skewness-0.53844997
Sum10749007
Variance8006.5184
MonotonicityNot monotonic
2023-07-08T16:41:06.612098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206.5835266 144
 
0.3%
47.4370575 122
 
0.2%
163.7785645 101
 
0.2%
43.04681396 99
 
0.2%
207.6810913 95
 
0.2%
112.1926041 94
 
0.2%
237.3153381 92
 
0.2%
217.5586548 90
 
0.2%
84.75294495 85
 
0.2%
298.7790222 85
 
0.2%
Other values (14445) 51481
97.9%
ValueCountFrequency (%)
0.06264029456 1
 
< 0.1%
0.1288120499 1
 
< 0.1%
0.2414538711 6
 
< 0.1%
0.2414805889 19
 
< 0.1%
0.2414855957 1
 
< 0.1%
0.2415168136 1
 
< 0.1%
0.241546616 11
 
< 0.1%
0.2419116646 1
 
< 0.1%
0.2419128269 18
 
< 0.1%
0.242034927 48
0.1%
ValueCountFrequency (%)
359.9884677 1
< 0.1%
359.9582626 1
< 0.1%
359.8437973 1
< 0.1%
359.7452144 1
< 0.1%
359.6898057 1
< 0.1%
359.6791834 1
< 0.1%
359.6781921 1
< 0.1%
359.6734114 1
< 0.1%
359.6481489 1
< 0.1%
359.6218424 1
< 0.1%

blade_angle
Real number (ℝ)

Distinct17794
Distinct (%)33.9%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.8897756
Minimum0
Maximum92.489998
Zeros26317
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T16:41:06.713553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.89733536
95-th percentile44.990002
Maximum92.489998
Range92.489998
Interquartile range (IQR)0.89733536

Descriptive statistics

Standard deviation14.078401
Coefficient of variation (CV)2.8791508
Kurtosis14.199439
Mean4.8897756
Median Absolute Deviation (MAD)0
Skewness3.6235445
Sum256654.54
Variance198.20138
MonotonicityNot monotonic
2023-07-08T16:41:06.805119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26317
50.1%
44.99000168 3008
 
5.7%
0.02449974513 610
 
1.2%
1.49000001 391
 
0.7%
89.98999786 312
 
0.6%
0.0489995709 258
 
0.5%
0.07349946834 161
 
0.3%
0.4900000095 111
 
0.2%
0.0979994285 104
 
0.2%
0.9900000095 82
 
0.2%
Other values (17784) 21134
40.2%
ValueCountFrequency (%)
0 26317
50.1%
0.0001666666552 4
 
< 0.1%
0.0001666666622 27
 
0.1%
0.0001754385918 2
 
< 0.1%
0.0003333333104 3
 
< 0.1%
0.0003333333201 7
 
< 0.1%
0.0003333333244 27
 
0.1%
0.000342105254 2
 
< 0.1%
0.0003508771836 7
 
< 0.1%
0.0003703703605 3
 
< 0.1%
ValueCountFrequency (%)
92.48999786 46
0.1%
92.47216492 1
 
< 0.1%
92.35845431 1
 
< 0.1%
92.20315841 1
 
< 0.1%
92.15666453 5
 
< 0.1%
92.15333303 4
 
< 0.1%
92.14683329 1
 
< 0.1%
92.14333344 9
 
< 0.1%
92.14333344 19
< 0.1%
92.13999939 3
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

Distinct35931
Distinct (%)68.5%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean65.99655
Minimum13.2275
Maximum76.6525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T16:41:06.895859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13.2275
5-th percentile46.386126
Q164.975987
median68.912499
Q370.819999
95-th percentile72.847367
Maximum76.6525
Range63.425
Interquartile range (IQR)5.8440123

Descriptive statistics

Standard deviation8.3977987
Coefficient of variation (CV)0.12724603
Kurtosis6.6433209
Mean65.99655
Median Absolute Deviation (MAD)2.4450005
Skewness-2.42662
Sum3464026.9
Variance70.523023
MonotonicityNot monotonic
2023-07-08T16:41:06.985436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.60000038 14
 
< 0.1%
70.03250008 11
 
< 0.1%
69.55999947 10
 
< 0.1%
69.98750038 10
 
< 0.1%
69.50250015 10
 
< 0.1%
69.97750053 10
 
< 0.1%
70.94749985 9
 
< 0.1%
70.50250015 9
 
< 0.1%
71.60250015 9
 
< 0.1%
70.125 9
 
< 0.1%
Other values (35921) 52387
99.7%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
13.22749996 1
< 0.1%
13.33500004 1
< 0.1%
13.40499973 1
< 0.1%
13.50749969 1
< 0.1%
13.55000019 1
< 0.1%
13.6050005 1
< 0.1%
13.63000011 1
< 0.1%
13.65999985 1
< 0.1%
13.67000008 1
< 0.1%
13.71500015 1
< 0.1%
ValueCountFrequency (%)
76.65250015 1
< 0.1%
76.53000031 1
< 0.1%
76.375 1
< 0.1%
76.225 1
< 0.1%
75.99473813 1
< 0.1%
75.9666659 1
< 0.1%
75.86500015 1
< 0.1%
75.85500031 1
< 0.1%
75.85250015 1
< 0.1%
75.85249977 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51311
Distinct (%)97.8%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10.862165
Minimum0
Maximum15.321017
Zeros1111
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T16:41:07.081720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.67804151
Q18.542439
median11.346362
Q314.324177
95-th percentile15.158696
Maximum15.321017
Range15.321017
Interquartile range (IQR)5.781738

Descriptive statistics

Standard deviation3.9502334
Coefficient of variation (CV)0.36366908
Kurtosis1.0546658
Mean10.862165
Median Absolute Deviation (MAD)2.8856476
Skewness-1.1391208
Sum570133.3
Variance15.604344
MonotonicityNot monotonic
2023-07-08T16:41:07.177596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1111
 
2.1%
0.0110000018 10
 
< 0.1%
0.01150000188 6
 
< 0.1%
0.01050000242 3
 
< 0.1%
0.02200000361 3
 
< 0.1%
0.02550000418 3
 
< 0.1%
0.02300000377 3
 
< 0.1%
0.01200000197 3
 
< 0.1%
8.137481689 2
 
< 0.1%
8.142611101 2
 
< 0.1%
Other values (51301) 51342
97.7%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
0 1111
2.1%
0.002955500269 1
 
< 0.1%
0.003813000396 1
 
< 0.1%
0.005106316806 1
 
< 0.1%
0.007560002385 1
 
< 0.1%
0.01050000242 3
 
< 0.1%
0.01050000265 2
 
< 0.1%
0.0110000018 10
 
< 0.1%
0.01125200186 1
 
< 0.1%
0.01150000188 6
 
< 0.1%
ValueCountFrequency (%)
15.32101692 1
< 0.1%
15.30761182 1
< 0.1%
15.30572395 1
< 0.1%
15.30473049 1
< 0.1%
15.30412352 1
< 0.1%
15.30386813 1
< 0.1%
15.30333805 1
< 0.1%
15.30232416 1
< 0.1%
15.29826701 1
< 0.1%
15.28918791 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct52453
Distinct (%)99.9%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1289.3804
Minimum-145.25874
Maximum1818.1705
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size410.8 KiB
2023-07-08T16:41:07.279767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-145.25874
5-th percentile82.156667
Q11015.6784
median1347.1025
Q31698.2031
95-th percentile1797.4821
Maximum1818.1705
Range1963.4293
Interquartile range (IQR)682.52463

Descriptive statistics

Standard deviation467.47911
Coefficient of variation (CV)0.36256104
Kurtosis1.0725356
Mean1289.3804
Median Absolute Deviation (MAD)340.68385
Skewness-1.1457893
Sum67677001
Variance218536.72
MonotonicityNot monotonic
2023-07-08T16:41:07.373871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1723.143888 2
 
< 0.1%
968.2321644 2
 
< 0.1%
968.507019 2
 
< 0.1%
969.1641026 2
 
< 0.1%
968.337759 2
 
< 0.1%
969.356163 2
 
< 0.1%
968.5162354 2
 
< 0.1%
968.6882915 2
 
< 0.1%
968.9338818 2
 
< 0.1%
967.636631 2
 
< 0.1%
Other values (52443) 52468
99.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
-145.2587381 1
< 0.1%
-62.29066551 1
< 0.1%
0.9917865358 1
< 0.1%
1.023379591 1
< 0.1%
1.041689957 1
< 0.1%
1.072646328 1
< 0.1%
1.086499862 1
< 0.1%
1.110869776 1
< 0.1%
1.123076677 1
< 0.1%
1.124196371 1
< 0.1%
ValueCountFrequency (%)
1818.170546 1
< 0.1%
1817.766792 1
< 0.1%
1815.020994 1
< 0.1%
1814.897461 1
< 0.1%
1814.094292 1
< 0.1%
1813.883709 1
< 0.1%
1813.078514 1
< 0.1%
1811.976654 1
< 0.1%
1811.869109 1
< 0.1%
1811.751245 1
< 0.1%

Nacelle ambient temperature (°C)
Real number (ℝ)

Distinct32353
Distinct (%)61.6%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11.432084
Minimum-0.41249999
Maximum36.259999
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)0.1%
Memory size410.8 KiB
2023-07-08T16:41:07.473649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.41249999
5-th percentile3.3000001
Q17.1049999
median10.7075
Q315.36
95-th percentile21.26325
Maximum36.259999
Range36.672499
Interquartile range (IQR)8.255

Descriptive statistics

Standard deviation5.6593388
Coefficient of variation (CV)0.49503999
Kurtosis0.083283499
Mean11.432084
Median Absolute Deviation (MAD)4.0074998
Skewness0.52703577
Sum600047.24
Variance32.028116
MonotonicityNot monotonic
2023-07-08T16:41:07.687862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5 86
 
0.2%
9 64
 
0.1%
7 58
 
0.1%
8.5 54
 
0.1%
7.099999905 53
 
0.1%
7.699999809 52
 
0.1%
6.400000095 50
 
0.1%
6.800000191 50
 
0.1%
10.80000019 49
 
0.1%
7.800000191 49
 
0.1%
Other values (32343) 51923
98.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
-0.412499994 1
 
< 0.1%
-0.400000006 2
< 0.1%
-0.3725000024 1
 
< 0.1%
-0.3300000131 2
< 0.1%
-0.2899999917 1
 
< 0.1%
-0.2100000083 1
 
< 0.1%
-0.200000003 1
 
< 0.1%
-0.1975000054 4
< 0.1%
-0.1925000101 1
 
< 0.1%
-0.1916666627 1
 
< 0.1%
ValueCountFrequency (%)
36.2599987 1
< 0.1%
36.24999886 1
< 0.1%
36.04999924 1
< 0.1%
36.04499931 1
< 0.1%
36.01999969 1
< 0.1%
36.01249981 1
< 0.1%
36.00749989 1
< 0.1%
35.97250042 1
< 0.1%
35.96052632 1
< 0.1%
35.90000134 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

Distinct36237
Distinct (%)69.0%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean66.444896
Minimum14.36
Maximum76.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T16:41:07.788557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.36
5-th percentile45.470875
Q164.0075
median70.55875
Q372.214999
95-th percentile73.845001
Maximum76.75
Range62.39
Interquartile range (IQR)8.2074988

Descriptive statistics

Standard deviation9.2229028
Coefficient of variation (CV)0.13880528
Kurtosis3.9511621
Mean66.444896
Median Absolute Deviation (MAD)2.5312496
Skewness-1.9706649
Sum3487559.7
Variance85.061935
MonotonicityNot monotonic
2023-07-08T16:41:07.878381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.37750015 11
 
< 0.1%
30.70000076 11
 
< 0.1%
73.75 10
 
< 0.1%
72.375 10
 
< 0.1%
72.55249977 10
 
< 0.1%
70.425 10
 
< 0.1%
73.70249977 10
 
< 0.1%
71.52500038 10
 
< 0.1%
71.34249992 10
 
< 0.1%
70.75 10
 
< 0.1%
Other values (36227) 52386
99.7%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
14.35999966 1
< 0.1%
14.38499928 1
< 0.1%
14.59749985 1
< 0.1%
14.6050005 1
< 0.1%
14.61750031 2
< 0.1%
14.61999989 1
< 0.1%
14.64249992 1
< 0.1%
14.69999981 2
< 0.1%
14.76249981 1
< 0.1%
14.77499962 1
< 0.1%
ValueCountFrequency (%)
76.74999962 1
< 0.1%
76.41250076 1
< 0.1%
76.30499954 1
< 0.1%
76.12000275 1
< 0.1%
76.08611043 1
< 0.1%
76.06750031 1
< 0.1%
76.06749992 1
< 0.1%
76.05750275 1
< 0.1%
76.05000305 1
< 0.1%
76.04999962 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

Distinct52486
Distinct (%)> 99.9%
Missing71
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean48.313922
Minimum2.9261531
Maximum203.70564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T16:41:07.976539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.9261531
5-th percentile4.9506304
Q129.752068
median45.771202
Q363.04923
95-th percentile99.16987
Maximum203.70564
Range200.77948
Interquartile range (IQR)33.297162

Descriptive statistics

Standard deviation27.529654
Coefficient of variation (CV)0.5698079
Kurtosis1.1680875
Mean48.313922
Median Absolute Deviation (MAD)16.587552
Skewness0.8049444
Sum2535949.4
Variance757.88187
MonotonicityNot monotonic
2023-07-08T16:41:08.076597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.44300218 2
 
< 0.1%
52.13734818 2
 
< 0.1%
95.05598364 2
 
< 0.1%
38.23603821 1
 
< 0.1%
20.06645989 1
 
< 0.1%
42.22742648 1
 
< 0.1%
26.10062661 1
 
< 0.1%
44.7460535 1
 
< 0.1%
19.62275081 1
 
< 0.1%
23.94779983 1
 
< 0.1%
Other values (52476) 52476
99.8%
(Missing) 71
 
0.1%
ValueCountFrequency (%)
2.926153075 1
< 0.1%
3.105866456 1
< 0.1%
3.15919385 1
< 0.1%
3.171262705 1
< 0.1%
3.199778843 1
< 0.1%
3.211501519 1
< 0.1%
3.211538941 1
< 0.1%
3.214562161 1
< 0.1%
3.224702352 1
< 0.1%
3.234806532 1
< 0.1%
ValueCountFrequency (%)
203.7056376 1
< 0.1%
199.1851276 1
< 0.1%
197.0186319 1
< 0.1%
195.4129723 1
< 0.1%
192.8822763 1
< 0.1%
190.6154131 1
< 0.1%
189.0354912 1
< 0.1%
188.0130789 1
< 0.1%
187.4152636 1
< 0.1%
183.8962725 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct52331
Distinct (%)99.7%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.3542588
Minimum0.16530025
Maximum23.731781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T16:41:08.180329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.16530025
5-th percentile2.2869999
Q14.4392426
median6.1429734
Q37.9773897
95-th percentile11.074279
Maximum23.731781
Range23.566481
Interquartile range (IQR)3.5381471

Descriptive statistics

Standard deviation2.7596665
Coefficient of variation (CV)0.43430187
Kurtosis0.99588675
Mean6.3542588
Median Absolute Deviation (MAD)1.7697075
Skewness0.6691846
Sum333522.34
Variance7.6157592
MonotonicityNot monotonic
2023-07-08T16:41:08.272733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.94364512 2
 
< 0.1%
8.196148801 2
 
< 0.1%
5.444833541 2
 
< 0.1%
5.734018826 2
 
< 0.1%
5.638867378 2
 
< 0.1%
7.821241999 2
 
< 0.1%
7.299264526 2
 
< 0.1%
6.230064487 2
 
< 0.1%
6.932933521 2
 
< 0.1%
6.007128882 2
 
< 0.1%
Other values (52321) 52468
99.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
0.1653002519 1
< 0.1%
0.1960690167 1
< 0.1%
0.2025377585 1
< 0.1%
0.2062502448 1
< 0.1%
0.2197503129 1
< 0.1%
0.2349475175 1
< 0.1%
0.2450813949 1
< 0.1%
0.2594064355 1
< 0.1%
0.2863314956 1
< 0.1%
0.2902876278 1
< 0.1%
ValueCountFrequency (%)
23.7317811 1
< 0.1%
21.99397895 1
< 0.1%
21.53966254 1
< 0.1%
21.00664401 1
< 0.1%
20.8478848 1
< 0.1%
20.5537915 1
< 0.1%
20.38625422 1
< 0.1%
20.29680004 1
< 0.1%
20.25041375 1
< 0.1%
20.23173985 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

Distinct52476
Distinct (%)> 99.9%
Missing71
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.79044
Minimum2.5152512
Maximum283.51459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T16:41:08.366356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.5152512
5-th percentile4.9319759
Q116.052702
median24.627358
Q335.645587
95-th percentile59.317918
Maximum283.51459
Range280.99934
Interquartile range (IQR)19.592885

Descriptive statistics

Standard deviation17.70924
Coefficient of variation (CV)0.63724216
Kurtosis6.751346
Mean27.79044
Median Absolute Deviation (MAD)9.5222203
Skewness1.8506205
Sum1458692.4
Variance313.61718
MonotonicityNot monotonic
2023-07-08T16:41:08.455964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.153301239 2
 
< 0.1%
4.574335307 2
 
< 0.1%
4.18824482 2
 
< 0.1%
35.34981704 2
 
< 0.1%
49.38143997 2
 
< 0.1%
27.83149381 2
 
< 0.1%
32.79262924 2
 
< 0.1%
18.74854469 2
 
< 0.1%
13.03123093 2
 
< 0.1%
32.12993622 2
 
< 0.1%
Other values (52466) 52469
99.8%
(Missing) 71
 
0.1%
ValueCountFrequency (%)
2.51525116 1
< 0.1%
2.706247652 1
< 0.1%
2.748020781 1
< 0.1%
2.761294208 1
< 0.1%
3.039628166 1
< 0.1%
3.088215674 1
< 0.1%
3.106091052 1
< 0.1%
3.127113289 1
< 0.1%
3.138592747 1
< 0.1%
3.149679184 1
< 0.1%
ValueCountFrequency (%)
283.5145942 1
< 0.1%
211.4814241 1
< 0.1%
188.9028709 1
< 0.1%
167.2321525 1
< 0.1%
164.7617163 1
< 0.1%
163.3057091 1
< 0.1%
160.3060441 1
< 0.1%
158.544274 1
< 0.1%
157.1001965 1
< 0.1%
156.9820875 1
< 0.1%

Metal particle count counter
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing71
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean496.505
Minimum482
Maximum506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T16:41:08.537472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum482
5-th percentile485
Q1492
median497
Q3503
95-th percentile506
Maximum506
Range24
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.1573175
Coefficient of variation (CV)0.014415399
Kurtosis-1.0874955
Mean496.505
Median Absolute Deviation (MAD)6
Skewness-0.37511173
Sum26061051
Variance51.227194
MonotonicityIncreasing
2023-07-08T16:41:08.617574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
504 6426
12.2%
492 6020
11.5%
493 4810
9.2%
502 3973
 
7.6%
505 3677
 
7.0%
498 3473
 
6.6%
485 3396
 
6.5%
486 3275
 
6.2%
497 3157
 
6.0%
506 3011
 
5.7%
Other values (15) 11271
21.4%
ValueCountFrequency (%)
482 952
 
1.8%
483 1185
 
2.3%
484 94
 
0.2%
485 3396
6.5%
486 3275
6.2%
487 5
 
< 0.1%
488 4
 
< 0.1%
489 799
 
1.5%
490 533
 
1.0%
491 462
 
0.9%
ValueCountFrequency (%)
506 3011
5.7%
505 3677
7.0%
504 6426
12.2%
503 2687
5.1%
502 3973
7.6%
501 1454
 
2.8%
500 54
 
0.1%
499 1166
 
2.2%
498 3473
6.6%
497 3157
6.0%

Interactions

2023-07-08T16:41:04.028880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:50.287761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.489141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.619766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.753065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:54.927034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.040812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.203652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:58.437433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.561346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.655753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:01.785844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.949189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.103365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:50.372344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.572154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.702191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.827235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.005590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.125789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.285422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:58.517117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.641461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.737807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:01.971100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.027268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.189857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:50.460495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.659650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.794425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.914575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.095268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.221761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.374606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:58.606359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.732532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.829113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.053760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.112485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.273754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:50.553139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.751239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.888167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.998950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.184404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.315257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.467257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:58.697689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.820399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.919291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.137824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.199131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.351592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:50.637081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.831260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.971408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:54.074552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.263964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.403188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.548426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:58.784793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.897692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.999976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.211527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.275977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.431632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:50.728054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.917947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.057115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:54.156504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.346243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.490270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.635794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:58.868544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.978511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:01.084973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.291586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.357232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.519558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:50.818516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.010522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.150526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:54.245028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.438421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.584935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.729778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:58.960938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.069338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:01.178666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.379307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.447240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.603281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:50.904479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.102132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.239966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:54.330576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.525362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.674715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.819003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.048290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.154824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:01.269015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.464629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.532184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.687696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.071125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.191339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.331473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:54.416478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.614242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.769881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.907277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.137101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.244624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:01.360011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.547811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.620091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.772224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.156733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.277405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.417135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:54.497427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.696951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.855482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.992761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.220634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.324708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:01.445807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.628200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.702288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.859554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.248069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.370866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.509296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:54.583467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.789947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:56.951590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:58.084212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.313276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.416555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:01.536670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.716686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.793252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:04.936341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.326045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.454076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.588264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:54.769366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.872511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.033002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:58.271763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.393695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.494091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:01.618121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.791428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.870436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:05.015365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:51.408165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:52.537001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:53.668839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:54.847642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:55.957157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:57.120806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:58.354664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:40:59.478545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:00.576510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:01.702784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:02.871262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T16:41:03.950811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T16:41:08.694395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.1700.151-0.1310.6820.9940.994-0.2150.8440.5660.9840.808-0.014
Wind direction (°)0.1701.0000.895-0.0560.1280.1680.168-0.0400.160-0.1010.1810.036-0.082
Nacelle position (°)0.1510.8951.000-0.0320.1050.1480.148-0.0420.139-0.1170.1610.019-0.086
blade_angle-0.131-0.056-0.0321.000-0.480-0.143-0.1450.137-0.3330.061-0.1140.032-0.043
Rear bearing temperature (°C)0.6820.1280.105-0.4801.0000.6810.678-0.0050.8870.3120.6680.4790.056
Rotor speed (RPM)0.9940.1680.148-0.1430.6811.0000.999-0.2100.8450.5610.9780.802-0.011
Generator RPM (RPM)0.9940.1680.148-0.1450.6780.9991.000-0.2200.8440.5600.9780.802-0.013
Nacelle ambient temperature (°C)-0.215-0.040-0.0420.137-0.005-0.210-0.2201.000-0.153-0.077-0.196-0.1330.148
Front bearing temperature (°C)0.8440.1600.139-0.3330.8870.8450.844-0.1531.0000.4230.8280.6440.021
Tower Acceleration X (mm/ss)0.566-0.101-0.1170.0610.3120.5610.560-0.0770.4231.0000.5320.8350.022
Wind speed (m/s)0.9840.1810.161-0.1140.6680.9780.978-0.1960.8280.5321.0000.788-0.009
Tower Acceleration y (mm/ss)0.8080.0360.0190.0320.4790.8020.802-0.1330.6440.8350.7881.000-0.003
Metal particle count counter-0.014-0.082-0.086-0.0430.056-0.011-0.0130.1480.0210.022-0.009-0.0031.000

Missing values

2023-07-08T16:41:05.130206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T16:41:05.329100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T16:41:05.672969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02019-01-01 00:00:00376.141663282.494476292.1936340.00000070.36499810.6158031261.9213878.64500070.86499838.2360385.80052819.262169482.0
12019-01-01 00:10:00348.686066290.551697292.1936340.00000069.22499810.2535631220.0596928.64500069.67749843.6404915.48704518.294140482.0
22019-01-01 00:20:00463.150360288.191071292.1936340.00000071.75499711.2758511338.9632578.66000171.76249748.0233315.76178818.789383482.0
32019-01-01 00:30:00512.391785290.157379292.1936340.00000072.59749611.6387661381.9490978.73000072.71499643.2380376.33774620.295347482.0
42019-01-01 00:40:00384.612122304.097015292.1936340.00000067.75499710.6781091270.1090098.56750068.13749732.9703795.57651615.820447482.0
52019-01-01 00:50:00201.260590312.497528306.1087340.00000066.0750058.9166331060.9575208.60000066.31250043.1941414.62286817.659321482.0
62019-01-01 01:00:00174.496964312.609528313.0473630.07349965.8775028.7462281041.2618418.59500065.65250451.3808254.51290322.204218482.0
72019-01-01 01:10:00203.102905303.017212313.0473630.00000065.9749988.9359931062.2104498.60000065.53749852.9384384.94912221.794411482.0
82019-01-01 01:20:00213.073578298.985535313.0473630.00150066.2150049.0873601081.3837898.53250065.78749841.9173974.96770317.318666482.0
92019-01-01 01:30:00159.261368290.418915311.6426390.07399865.3724988.4930181010.7064218.49250064.96499647.9220704.47503619.388697482.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
525502019-12-31 22:20:00175.754595105.480339125.3630070.09849865.8299998.7118561036.0739026.850065.37500059.2432814.57033628.186711506.0
525512019-12-31 22:30:00190.74148199.586301125.3630070.00000065.7450008.7875401044.8131036.797565.26250056.9532044.53226428.248822506.0
525522019-12-31 22:40:00134.58708390.608833104.5079430.14749864.6150008.290898986.8079686.725063.93250067.5761694.08958531.775141506.0
525532019-12-31 22:50:00192.86277989.728070101.2167590.02450065.0975018.8248451050.4770986.615064.24499957.8721484.56992728.366423506.0
525542019-12-31 23:00:00172.32552194.381436101.2167590.00000065.1324998.6106531023.1418956.530064.53999980.1622814.18258430.066501506.0
525552019-12-31 23:10:00180.61986192.194368101.2167590.00000065.3149998.6819791033.8922526.267564.68750071.8385144.46109521.168742506.0
525562019-12-31 23:20:00192.81058492.082155101.2167590.00000065.7400018.7888471046.2378296.110065.33750168.8402234.48504121.273681506.0
525572019-12-31 23:30:00259.13995996.061557101.2167590.00000066.3025009.4867801128.0027876.160065.99250064.6405484.73552321.640125506.0
525582019-12-31 23:40:00306.731685103.571461101.2167590.00000067.3850019.9813171186.9095336.257567.35000254.7067465.10358123.263102506.0
525592019-12-31 23:50:00293.376791108.657192101.2167590.00000067.3175009.8291261169.5760486.367567.44000144.6196394.81402419.112631506.0